Color image segmentation using neuro-fuzzy system in a novel optimized color space
| Autor: | B. Somayeh Mousavi, Navid Razmjooy, Fazlollah Soleymani |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Color histogram
business.industry Color image Binary image ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Scale-space segmentation Color balance Pattern recognition Image segmentation Image texture Artificial Intelligence Computer Science::Computer Vision and Pattern Recognition Computer vision Artificial intelligence Range segmentation business Software Mathematics |
| Zdroj: | Neural Computing and Applications. 23:1513-1520 |
| ISSN: | 1433-3058 0941-0643 |
| Popis: | Segmentation is one of the most important pre-processing steps toward pattern recognition and image understanding. It is often used to partition an image into separate regions, which ideally correspond to different real-world objects. In this paper, novel color image segmentation is proposed and implemented using fuzzy inference system in optimized color space. This system, which is designed by neuro-adaptive learning technique, applies a sample image as an input and can reveal the likelihood of being a special color for each pixel through the image. The intensity of each pixel shows this likelihood in the gray-level output image. After choosing threshold value, a binary image is obtained, which can be applied as a mask to segment desired color in input image. Besides using fuzzy systems, optimizing color space for segmentation is another feature of proposed method. This optimizing is implemented by genetic algorithms and influence on system accuracy. Two applications of developed method are discussed, and still it could be applicable in wide range of color image segmentation or object detection purposes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink